Examples of the image pairs of different modals. (a) The SEN 12 MS dataset. a1. Optical a2. SAR a3. NIR a4. SWIR (b) The RGB-NIR Scene dataset. b1. RGB b2. Near-Infrared (c) The WHU-OPT-SAR dataset. c1. Optical c2. SAR (d) The Optical-SAR dataset. d1. Optical d2. SAR (e) The BrainWeb dataset. e1. T1 e2. T2 (f) The NYU-Depth V2 dataset. f1. Depth f2. RGB (g) The UV/Green Image dataset. g1. UV g2. Green.

<p>Examples of the image pairs of different modals. (a) The SEN 12 MS dataset. a1. Optical a2. SAR a3. NIR a4. SWIR (b) The RGB-NIR Scene dataset. b1. RGB b2. Near-Infrared (c) The WHU-OPT-SAR dataset. c1. Optical c2. SAR (d) The Optical-SAR dataset. d1. Optical d2. SAR (e) The BrainWeb datase...

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Bibliographic Details
Main Author:	Yide Di (20969124) (author)
Other Authors:	Yun Liao (160524) (author), Hao Zhou (136535) (author), Kaijun Zhu (18283913) (author), Qing Duan (541846) (author), Junhui Liu (2063140) (author), Mingyu Lu (2333083) (author)
Published:	2025
Subjects:	Sociology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified multimodal image applications image feature matching experimental results demonstrate data augmentation algorithm across different modals ufm exhibits versatility div >< p imbalanced modal datasets specific modals specific datasets sparse data wide spectrum ufm excels ufm </ trained model staged pre remains challenging modal images foundational task computer vision
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